Artificial intelligence code review startup CodeRabbit Inc. has raised $60 million in a Series B funding, a round that it says demonstrates its growing importance at a time when AI-generated code is being spat out faster than ever.

Today’s round was led by Scale Venture Partners and saw participation from NVentures, Nvidia Corp.’s venture capital arm and existing investors CRV, Harmony Partners, Flex Capital, Engineering Capital and Pelion Venture Partners. It brings its total amount raised to date to more than $88 million.

CodeRabbit is the creator of a generative AI-enabled code review tool that’s designed to work like a human colleague for developers, automatically reviewing the code they create, as it is written, providing context-aware feedback on pull requests within minutes. The tool can integrate with code repositories such as GitHub and GitLab, providing continuous, incremental reviews of developer’s code, reducing the amount of time and effort that would otherwise go into reviewing code manually.

At the core of CodeRabbit is a unique context engineering approach, where it dynamically gathers intelligence from dozens of sources that are deemed critical for code reviews. That includes custom review instructions, historical pull requests, code graphs, coding agent guidelines, architectural documents, Jira and Linear tickets.

It does all this to provide its underlying algorithm with the information it needs to make accurate review comments on code written by either humans or AI agents. It will point out bugs, identify security flaws and code refactors, recommend architectural improvements and enforce policies.

Unlike tools that operate in isolation, CodeRabbit is deeply integrated across the development lifecycle. It works inside popular code editors like VS Code, Cursor, and Windsurf and in Git platforms like GitHub, GitLab, Azure DevOps and Bitbucket. It also integrates with AI coding agents like Cursor, Claude Code, and GitHub Copilot, enabling a fully AI-powered loop from generation to review to implementation without breaking the developer’s flow state.

Not surprisingly, CodeRabbit believes it’s superior to static code analysis tools and linters that have traditionally been used for code review. Such rules-based tools are less flexible, and therefore less reliable, resulting in higher false-positive rates. Meanwhile, traditional peer reviews conducted by humans are time-consuming and subjective, meaning they can’t always be relied upon, the startup says.

Rampant growth

The growth in CodeRabbit’s popularity testifies to the truth of these claims and proof of its rising importance as developer teams accelerate productivity through the adoption of AI “vibe coding” tools. Vibe coding makes it possible for developer teams to generate much bigger volumes of code than before, but it also creates a bottleneck in the testing process that compromises those velocity gains. CodeRabbit provides relief from that.

In the last year, the company claims to have grown its revenue by 10 times and its head count by double, while adding more than 8,000 paying customers. It has become the No. 1 application on the GitHub Marketplace, and has been integrated with more than 100,000 open-source software projects.

Scale Venture Partners’ Andy Vitus said CodeRabbit’s popularity is what convinced him to back the company. “CodeRabbit has emerged as the clear leader in AI code reviews, acting as the governance layer AI development desperately needs,” he said. “It has built a code review system that works well even for large codebases while delivering the accuracy and rigor of a senior engineer.”

Holger Mueller of Constellation Research Inc. said CodeRabbit has shown that AI itself is the most reliable way to ensure the accuracy of AI-generated code. “AI has already demonstrated an ability to review human-written software, and now it’s doing the same for machine-generated code too,” the analyst declared. “But if CodeRabbit is to succeed, it will be under pressure to ensure the integrity of AI-created code day in, day out, without making any mistakes. Time will tell.”

Alongside today’s funding, CodeRabbit announced new capabilities in its code review tool that aim to make context-aware AI code reviews even more accessible and impactful. For instance, it can now be integrated directly within the developer’s command line interface, and it can be applied to “pre-merge checks,” automatically generating tests to help teams implement guardrails before they merge pull requests.

In addition, CodeRabbit is planning the launch of a new Model Context Protocol client. That will enable it to fetch additional context from sources beyond code, such as feature requirements and engineering documents.

CodeRabbit co-founder and Chief Executive Harjot Gill said it’s clear that AI-generated code is here to stay in the enterprise. Its adoption necessitates that developer teams have access to a centralized knowledge base and independent governance layer, which is what his company provides.

“Code review is the most critical quality gate in the agentic software lifecycle, and you have to build AI agents that are context-aware so they can catch the bugs that are hardest to find,” he explained. “That’s exactly what we’ve built.”

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